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How do we remember faces and other patterns?
I’ve always suspected that what we remember is not the pattern per se, but rather a kind of feeling uniquely connected to that pattern. So, seeing a somewhat familiar face, we don’t really try to match it with a model template, pixel-by-pixel, stored in our brain somewhere, but rather, it’s the same perception (sometimes can be emotional) response that repeats itself, something similar to a deja vu (however with stronger intensity), which gives us the certainty about the identity of the face.
That, may be extended to the memory of landscape, drawings, and music.
For decades, computer scientists have been working on building associative memory models using artificial neural network models. No matter it is auto-associative, hetero-associative, bidirectional memory, or adding a bit dynamics – chaotic associative memory, the computational model is almost always built on the Hebb rule, using correlation matrix to store the spatial patterns per se.
Neuroscientists have long found the relevance of eye movement and saliency detection in object recognition. In light of the recent neuroscience finding, The Brain ‘Joins The Dots’ When Drawing A Cartoon Face From Memory, it becomes clearer that we should build our memory models otherwise, incorporating key points and their tracking configured in an ‘action plan’. In other words, not only is spatial correlation important, but also the timing of when attention shifts. Or it could be the sequence of firing neurons along the timeline, and its pattern, that matters? Therefore we have to build large-scale parallel systems so as to cope with the memory formation and retrieval… but how?
Well, I don’t really see an answer to these questions. Do they make sense to you?
